We published methods for handling missing data for a project to develop a breast cancer risk model that includes mammographic density, weight, family history, age at first live birth and number of previous breast biopsies. The data presented a number of challenges, because mammographic density measurements were only available on subsets of the study population. The resulting paper on projecting absolute risk using mammographic density has been published, and software is available through the Biostatistics Branch web site. We published on whether seven recently identified common single nucleotide polymorphisms (SNPs) associated with breast cancer risk could improve the utility of NCIs Breast Cancer Risk Assessment Tool (BCRAT), which is based on simple questionnaire data. We evaluated four public health applications. Adding the seven SNPs to BCRAT produced only minimal improvements in the following applications: deciding whether to take tamoxifen to prevent breast cancer; deciding whether to have a mammogram; and allocating mammograms when there is not enough money to screen all women. When women were cross-classified on their risks from BCRAT and their risks from a model that also included the seven SNPs, risk classifications changed for some women. Thus, there is a possibility that the model with SNPs could be useful for some women, but the model with SNPs would need to be validated in independent cohort data to have confidence that the reclassification of risk was accurate. Very similar results were obtained when three additional recently discovered SNPs were also included in the model, along with the seven previously studied SNPs. We also evaluated the performance of a risk model using 10 newly identified genetic variants as well as family history, age at first live birth and number of previous breast biopsies in 6,000 cases and 6,000 controls from 5 studies. We addressed whether a woman from a high risk family known to carry mutations in BRCA1 or BRCA2 genes had above average risk of breast cancer even if she was found not to carry a mutation. Because most of the familial correlation in breast cancer risk is not due to BRCA1 or BRCA2 mutations, and because most high risk families are ascertained because several members are affected, there is reason to believe that such a woman remains at higher risk than the general population, even though the risk is not as high as for a mutation carrier. Studies are in progress to quantify the extent of such risk. BRCAPRO is a model to predict who in a family carries a mutation in BRCA1 or BRCA2, based on family history of breast and ovarian cancer. We extended BRCAPRO to account for family members not developing certain cancers associated with BRCA1 or BRCA2 mutations. We account for men not developing prostate cancer and thereby correct an upward bias in the estimate of the probability of carrying a mutation.<br> <br> We developed an absolute risk prediction model for colorectal cancer from population-based case-control data and age-specific rates from the Surveillance, Epidemiology, and End Results (SEER) program. We also developed a questionnaire suitable for self-administration for risk projections. The model includes separate relative risks, attributable risks and SEER rates for proximal, distal, and rectal cancer, which were combined to estimate overall colorectal cancer risk, separately for men and women. For men, the model includes a cancer-negative sigmoidoscopy/colonoscopy in the last 10 years, polyp history in the last 10 years, history of CRC in first-degree relatives, aspirin and nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, body mass index (BMI), current leisure-time vigorous activity, and vegetable consumption. For women, the model includes sigmoidoscopy/colonoscopy, polyp history, history of CRC in first-degree relatives, aspirin and NSAID use, BMI, leisure-time vigorous activity, vegetable consumption, hormone-replacement therapy (HRT), and estrogen exposure on the basis of menopausal status. This model is potentially useful for counseling, for designing research intervention studies, and for other applications. The models were independently validated using data from the AARP cohort. Calibration was good both for men and women; the discriminatory accuracy, measured as the age-specific area under the receiver operating characteristic curve was 0.61, in men and women. The model is available through http://www.cancer.gov/ColorectalCancerRisk/.<br> <br> We criticized a report on a commercial ovarian cancer screening test, OvaSure, that over-estimated the positive predictive value of the assay by assuming that the disease prevalence in the population equaled that in the case-control validation sample. This report influenced the Society of Gynecologic Oncologists and the Food and Drug Administration in reaching the conclusion that additional research is needed to fully evaluate the test before it is offered to women outside the research setting.<br> <br> We are constructing a model based on HPV testing to help guide diagnostic testing and treatment of women at risk of cervical cancer. We advocate replacing complex clinical algorithms with a risk tool to guide management and thereby integrate new technologies and information into screening faster, more rationally, and more cost-effectively.<br> <br> We published methods that show how risk models can be used to allocate resources for public health applications, such as mammographic screening, under cost constraints. Various optimal strategies for resource allocation can be expressed in term of the Lorenz curve of the distribution of risk in the population. More discriminating risk models yield more efficient allocation of resources.<br> <br> We reviewed designs and analytic methods for estimating absolute risk from genetic mutations and compared risks estimated from population-based versus family studies. Relative risks from family studies can be higher than from population-based studies because within family comparisons are not attenuated by random familial effects.